2012年3月
High-Risk Ovarian Cancer Based on 126-Gene Expression Signature Is Uniquely Characterized by Downregulation of Antigen Presentation Pathway
CLINICAL CANCER RESEARCH
- 巻
- 18
- 号
- 5
- 開始ページ
- 1374
- 終了ページ
- 1385
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1158/1078-0432.CCR-11-2725
- 出版者・発行元
- AMER ASSOC CANCER RESEARCH
Purpose: High-grade serous ovarian cancers are heterogeneous not only in terms of clinical outcome but also at the molecular level. Our aim was to establish a novel risk classification system based on a gene expression signature for predicting overall survival, leading to suggesting novel therapeutic strategies for high-risk patients.
Experimental Design: In this large-scale cross-platform study of six microarray data sets consisting of 1,054 ovarian cancer patients, we developed a gene expression signature for predicting overall survival by applying elastic net and 10-fold cross-validation to a Japanese data set A (n = 260) and evaluated the signature in five other data sets. Subsequently, we investigated differences in the biological characteristics between high-and low-risk ovarian cancer groups.
Results: An elastic net analysis identified a 126-gene expression signature for predicting overall survival in patients with ovarian cancer using the Japanese data set A (multivariate analysis, P = 4 x 10(-20)). We validated its predictive ability with five other data sets using multivariate analysis (Tothill's data set, P = 1 x 10(-5); Bonome's data set, P = 0.0033; Dressman's data set, P = 0.0016; TCGA data set, P = 0.0027; Japanese data set B, P = 0.021). Through gene ontology and pathway analyses, we identified a significant reduction in expression of immune-response-related genes, especially on the antigen presentation pathway, in high-risk ovarian cancer patients.
Conclusions: This risk classification based on the 126-gene expression signature is an accurate predictor of clinical outcome in patients with advanced stage high-grade serous ovarian cancer and has the potential to develop new therapeutic strategies for high-grade serous ovarian cancer patients. Clin Cancer Res; 18(5); 1374-85. (C)2012 AACR.
Experimental Design: In this large-scale cross-platform study of six microarray data sets consisting of 1,054 ovarian cancer patients, we developed a gene expression signature for predicting overall survival by applying elastic net and 10-fold cross-validation to a Japanese data set A (n = 260) and evaluated the signature in five other data sets. Subsequently, we investigated differences in the biological characteristics between high-and low-risk ovarian cancer groups.
Results: An elastic net analysis identified a 126-gene expression signature for predicting overall survival in patients with ovarian cancer using the Japanese data set A (multivariate analysis, P = 4 x 10(-20)). We validated its predictive ability with five other data sets using multivariate analysis (Tothill's data set, P = 1 x 10(-5); Bonome's data set, P = 0.0033; Dressman's data set, P = 0.0016; TCGA data set, P = 0.0027; Japanese data set B, P = 0.021). Through gene ontology and pathway analyses, we identified a significant reduction in expression of immune-response-related genes, especially on the antigen presentation pathway, in high-risk ovarian cancer patients.
Conclusions: This risk classification based on the 126-gene expression signature is an accurate predictor of clinical outcome in patients with advanced stage high-grade serous ovarian cancer and has the potential to develop new therapeutic strategies for high-grade serous ovarian cancer patients. Clin Cancer Res; 18(5); 1374-85. (C)2012 AACR.
- リンク情報
-
- DOI
- https://doi.org/10.1158/1078-0432.CCR-11-2725
- PubMed
- https://www.ncbi.nlm.nih.gov/pubmed/22241791
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000301040700021&DestApp=WOS_CPL
- URL
- http://europepmc.org/abstract/med/22241791
- URL
- http://orcid.org/0000-0002-2254-3378
- ID情報
-
- DOI : 10.1158/1078-0432.CCR-11-2725
- ISSN : 1078-0432
- ORCIDのPut Code : 9955769
- PubMed ID : 22241791
- Web of Science ID : WOS:000301040700021